Activation functions play a decisive role in determining the capacity of Deep Neural
Networks as they enable neural networks to capture inherent nonlinearities present in data …

In the era of big data, efficient data processing has become a crucial issue for scientific
development. Image classification, as one of the core tasks in the field of computer vision …

The reason behind CNNs capability to learn high-dimensional complex features from the
images is the non-linearity introduced by the activation function. Several advanced …

Deep learning architectures which are exceptionally deep have exhibited to be incredibly
powerful models for image processing. As the architectures become deep, it introduces …

Deep convolutional neural networks (CNNs) represent the state-of-the-art model structure in
image classification problems. However, deep CNNs suffer from issues of interpretability …

Deep neural network and Machine learning are a latest emerging concept in the field of data
science. Due to multi-layer hierarchical feature extraction in conjunction with control …

Deep convolutional neural network used for image classification is an important part of deep
learning and has great significance in the field of computer vision. Moreover, it helps …

Recent studies have shown that the choice of activation function can significantly affect the
performance of deep learning networks. However, the benefits of novel activation functions …

ABSTRACT This paper introduces Learnable Extended Activation Function (LEAF)-an
adaptive activation function that combines the properties of squashing functions and rectifier …

Numerous deep learning architectures have been developed as a result of activation
functions (AFs), which are crucial for allowing deep neural networks to deal with intricate …